Explore our Animal Image Classification Dataset with 15 classes of preprocessed images (224x224) ready for deep learning and AI applications.

recruit-jp/japanese-image-classification-evaluation-dataset

  Overview

Developed by: Recruit Co., Ltd. Dataset type: Image Classification Language(s): Japanese LICENSE: CC-BY-4.0

More details are described in our tech blog post.

日本語CLIP学習済みモデルとその評価用データセットの公開

  Dataset Details

This dataset is comprised of four image classification tasks related to concepts and things unique to Japan. Specifically, is consists of the following tasks.

jafood101: Image… See the full description on the dataset page: https://huggingface.co/datasets/recruit-jp/japanese-image-classification-evaluation-dataset.

The complete set of images have been classified among two classes i.e. healthy and diseased. First, the acquired images are classified and labeled conferring to the plants. The plants were named ranging from P0 to P11. Then the entire dataset has been divided among 22 subject categories ranging from 0000 to 0022. The classes labeled with 0000 to 0011 were marked as a healthy class and ranging from 0012 to 0022 were labeled diseased class. This is a collection of about 4503 images of which contains 2278 images of healthy leaf and 2225 images of the diseased leaf. Twelve plants named as Mango, Arjun, Alstonia Scholaris, Guava, Bael, Jamun, Jatropha, Pongamia Pinnata, Basil, Pomegranate, Lemon, and Chinar have been selected. Leaf images of these plants in healthy and diseased condition have been acquired and divided between two separate modules.

The images are in the ImageNet structure, with each class having its own folder containing the respective images. The images have a resolution of 256x256 pixels.

Dataset Details:

• Total number of classes: 14
• Total number of images: 3279
• Resolution: 256x256 pixels
• Image format: JPG

If you find this dataset useful or interesting, please don't forget to show your support by Upvoting! 🙌👍

Data Collection Methodology:

To create this dataset, - I searched for each PC part on Google Images and extracted the image links. - I then downloaded the full-size images from the original source and converted them to JPG format with a resolution of 256 pixels. - During the process, most images were downscaled, with only a very few being upscaled. - Finally, I manually went over all the images and deleted any that didn't fit well for image classification.

Potential Task Ideas:

1. Train an image classification model using popular architectures like ViT, ResNet, or EfficientNet.
2. Perform transfer learning on this dataset using pre-trained models.
3. Explore different data augmentation techniques to enhance model performance.
4. Fine-tune existing models to improve classification accuracy.
5. Compare the performance of different models on this dataset.
6. Use the dataset as a benchmark for evaluating new image classification techniques.

Class Naming Convention:

All files are named in ImageNet style. ```shell Kingdom ├── class_1 │ ├── 1.jpg │ └── 2.jpg ├── class_2 │ ├── 1.jpg │ └── 2.jpg └── class_3 ├── 1.jpg └── 2.jpg

**I have not divided the dataset into train,val,test so that you can decide on the split ratios.**

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Photo by <a href="https://unsplash.com/@zelebb?utm_content=creditCopyText&utm_medium=referral&utm_source=unsplash">Andrey Matveev</a> on <a href="https://unsplash.com/photos/a-close-up-of-two-computer-fans-on-a-yellow-background-8hkotoCEI5o?utm_content=creditCopyText&utm_medium=referral&utm_source=unsplash">Unsplash</a>

resolverkatla/Intel-Image-Classification dataset hosted on Hugging Face and contributed by the HF Datasets community

1) Data Introduction • The Sports balls - multiclass image classification Dataset is a computer vision dataset for multi-class image classification, designed to classify images of balls used in various sports. The dataset consists of 15 categories, including basketballs, footballs (soccer), rugby balls, table tennis balls, and more.

2) Data Utilization (1) Characteristics of the Sports balls - multiclass image classification Dataset: • Some balls in the dataset feature intentional visual alterations (e.g., balls painted to resemble other types), enabling a precise evaluation of a model’s generalization and discrimination capabilities.

(2) Applications of the Sports balls - multiclass image classification Dataset: • Sports Ball Classification Model Development: This dataset can be used to train deep learning-based image classification models that automatically recognize and categorize various types of sports equipment. • Development of Sports-related Applications: The dataset is suitable for building sports equipment recognition systems, AR-based educational tools, and video-based sports analysis systems.

Cats And Dogs Image Classification

## Overview

Cats And Dogs Image Classification is a dataset for classification tasks - it contains Cats And Dogs annotations for 2,000 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This repository contains three microscopy image datasets used for cellular image analysis, specifically for image classification, object detection, and image reconstruction tasks. These datasets were previously published by our group and include DCTL (https://doi.org/10.1093/bioinformatics/btaa513), GFS-ExtremeNet (https://doi.org/10.1128/mSystems.00840-19), and COMI (https://doi.org/10.1016/j.csbj.2022.04.003).

Image Classification

## Overview

Image Classification is a dataset for object detection tasks - it contains EMPTY OVER RIPE ROTTEN UNDER UN annotations for 2,215 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

Food Image Classification

## Overview

Food Image Classification is a dataset for classification tasks - it contains Objects annotations for 9,983 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Satellite Image Classification

## Overview

Satellite Image Classification is a dataset for classification tasks - it contains Objects annotations for 2,000 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The Alpaca Dataset is a collection of JPEG images designed for binary image classification tasks, classifying images as 'Alpaca' or 'Not Alpaca'. It is suitable for transfer learning, educational projects, and proof-of-concept applications in computer vision.

THIS DATASET IS PROVIDED TO ANYONE WHO WISHES TO USE IT. THERE ARE NO RESTRICTIONS ON ITS USE.

Dataset Overview

This page contains a modified Cocos dataset along with details about the dataset used.

File Descriptions

imgs.zip - Train: 🚂 This folder contains the training set, which can be split into train/validation data for model training. - Test: 🧪 Your trained models should be used to produce predictions on the test set.

labels.zip - categories.csv: 📝 This file lists all the object classes in the dataset, ordered according to the column ordering in the train labels file. - train_labels.csv: 📊 This file contains data regarding which image contains which categories.

Explore our comprehensive Tree Nuts Image Classification Dataset, meticulously curated to support advanced machine learning models.

MULTI-TEMPORAL REMOTE SENSING IMAGE CLASSIFICATION - A MULTI-VIEW APPROACH VARUN CHANDOLA AND RANGA RAJU VATSAVAI Abstract. Multispectral remote sensing images have been widely used for automated land use and land cover classification tasks. Often thematic classification is done using single date image, however in many instances a single date image is not informative enough to distinguish between different land cover types. In this paper we show how one can use multiple images, collected at different times of year (for example, during crop growing season), to learn a better classifier. We propose two approaches, an ensemble of classifiers approach and a co-training based approach, and show how both of these methods outperform a straightforward stacked vector approach often used in multi-temporal image classification. Additionally, the co-training based method addresses the challenge of limited labeled training data in supervised classification, as this classification scheme utilizes a large number of unlabeled samples (which comes for free) in conjunction with a small set of labeled training data.

The E-commerce Product Image Classification Dataset includes 18,175 images across 9 major product categories, curated from Amazon, Walmart, Google, and web scraping. Designed for training CNNs in product categorization and improving e-commerce user experience.

1) Data Introduction • The Vehicle Image Classification Dataset was developed to classify various types of vehicles, and is organized into seven categories: auto rickshaws, bikes, cars, motorcycles, planes, ships, and trains.

2) Data Utilization (1) Characteristics of the Vehicle Image Classification Dataset: • The dataset includes a diverse range of vehicle types across land, sea, and air, enabling broad applications in multi-class classification tasks. • It allows for detailed learning of the fine structural features of vehicles, making it suitable for developing high-precision classification models.

(2) Applications of the Vehicle Image Classification Dataset: • Development of Vehicle Type Classification Models: The dataset can be used to train and evaluate AI models that classify various types of vehicles. • Research on Autonomous Driving and Smart Transportation Systems: It serves as foundational data for vision-based research in autonomous vehicles and traffic monitoring systems.

Intel Image Classification

The Intel Image Classification dataset contains images of natural scenes categorized into six classes:

Buildings

Forest

Glacier

Mountain

Sea

Street

  📆 Content

The dataset contains ~25,000 images of size 150x150 pixels.

Images are evenly distributed across 6 categories: {'buildings' -> 0, 'forest' -> 1, 'glacier' -> 2, 'mountain' -> 3, 'sea' -> 4, 'street' -> 5 }

It is divided into three parts:

Training set: ~14… See the full description on the dataset page: https://huggingface.co/datasets/sfarrukhm/intel-image-classification.

About Dataset (strawberries, peaches, pomegranates) Photo requirements: 1-White background 2-.jpg 3- Image size 300*300 The number of photos required is 250 photos of each fruit when it is fresh and 250 photos of each Fruit Dataset for Classification when it is rotten. Total 1500 images

Diverse Collection With a diverse collection of Product images, the files provides an excellent foundation for developing and testing machine learning models designed for image recognition and allocation. Each image is captured under different lighting conditions and backgrounds, offering a realistic challenge for algorithms to overcome.

Real-World Applications The variability in the dataset ensures that models trained on it can generalize well to real-world scenarios, making them robust and reliable. The dataset includes common fruits such as apples, bananas, oranges, and strawberries, among others, allowing for comprehensive training and evaluation.

Industry Use Cases One of the significant advantages of using the Fruits Dataset for Classification is its applicability in various fields such as agriculture, retail, and the food industry. In agriculture, it can help automate the process of fruit sorting and grading, enhancing efficiency and reducing labor costs. In retail, it can be used to develop automated checkout systems that accurately identify fruits, streamlining the purchasing process.

Educational Value The dataset is also valuable for educational purposes, providing students and educators with a practical tool to learn and teach machine learning concepts. By working with this dataset, learners can gain hands-on experience in data preprocessing, model training, and evaluation.

Conclusion The Fruits Dataset for Classification is a versatile and indispensable resource for advancing the field of image classification. Its diverse and high-quality images, coupled with practical applications, make it a go-to dataset for researchers, developers, and educators aiming to improve and innovate in machine learning and computer vision.

This dataset is sourced from Kaggle.